Poster "interpretable machine learning" Papers
8 papers found
Causally Reliable Concept Bottleneck Models
Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis et al.
NeurIPS 2025posterarXiv:2503.04363
5
citations
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Jie Yang, Yuwen Wang, Kaixuan Chen et al.
ICLR 2025posterarXiv:2505.00364
3
citations
MIX: A Multi-view Time-Frequency Interactive Explanation Framework for Time Series Classification
Viet-Hung Tran, Ngoc Phu Doan, Zichi Zhang et al.
NeurIPS 2025poster
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, haichen zhou et al.
ICML 2024poster
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
ICML 2024poster
Post-hoc Part-Prototype Networks
Andong Tan, Fengtao ZHOU, Hao Chen
ICML 2024poster
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Gautam Machiraju, Alexander Derry, Arjun Desai et al.
ICML 2024poster
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Floris Holstege, Bram Wouters, Noud van Giersbergen et al.
ICML 2024poster